Bond Prices Research Articles

Inflation, Equity Market Volatility, and Bond Prices: Evidence from G7 Countries

This study examines the impacts of the US inflation rate on the bond prices of G7 countries across different maturities using inflation-induced equity market volatility (EMV) to better account for bond price determinants. The regression model, a GED-GARCH (1,1) procedure, is adopted to deal with the volatility clustering and fat tail features in bond return estimation. The testing results indicate that the inflation rate has a negative effect on bond returns across different maturities, although an exception occurs for longer maturities in Japan. Evidence shows that US inflation has a significant impact on bond returns for the non-US G7 countries. The negative effects from US inflation are more profound than those from the domestic market (expect in Japan). This study introduces the equity market volatility arising from inflation or the Fed’s interest rate change; this variable produces market volatility that has a positive effect on bond returns, offsetting part of the original negative effect from a rise in inflation.

Research on Pricing Model of Chinese Convertible Bonds

This paper aims to create a new model for convertible bond pricing based on convertible bond rules and convertible bond strategies. And based on the concept of value investment, this paper analyzes and deduces various situations of convertible bonds by using future cash flow discount thinking. In addition, two new options that achieve the conditional call price and options that exceed the conditional call price are presented, and a more flexible formulation of the model for computing the price of convertible bonds is derived. However, this model requires the user to have some experience with convertible bonds. This might make a bit of sense for academic development and could help people better understand and participate in the convertible bond market.

The Impact of Credit Rating on Pricing of Green Bond Issues - An Empirical Analysis Based on the TIC Model

As an important part of green finance, green bonds have become more and more obvious in the market, and their pricing has received more and more attention. As an important factor affecting bond pricing, it is necessary to explore the impact of credit rating on the pricing of green bond issues. This paper focuses on exploring the impact of credit rating on the pricing of green bond issuance by empirically analyzing the data of some green bonds issued in 2018-2022, and finds that: the credit rating of green bonds has a significant impact on their issuance interest rate, and the higher the credit rating, the lower the financing cost of green bonds; the issuance period, the issuance scale and the whether to set up the put provision and the call provision also affect the financing cost of green bond.

Predicting the Non-Return of Chonsei Lease Deposits in the Republic of Korea

Chonsei, a Korean housing lease system, enables landlords to acquire direct housing purchase funds without mortgages and offers tenants a cost-effective rental option. However, public concerns have arisen about potential landlord defaults, causing financial distress for tenants. This study examined the risk of non-return of the Chonsei deposit and developed a default prediction model using Chonsei contract data from the Korea Housing and Urban Guarantee Corporation. Starting with the components from Merton’s bond pricing model, we included variables that reflect contract-specific factors, macroeconomic conditions, and the Korean Chonsei practices. The findings revealed that higher house price volatility, elevated debt-to-house value, and risk-free interest rates positively correlate with non-return risk. Meanwhile, certain factors, such as longer remaining maturity, favorable macroeconomic conditions, and rising market Chonsei price trends, demonstrated negative correlations with non-return risk. Consequently, a logistic regression-based default prediction model, with eight risk factors that predict the deposit non-return, was suggested. By identifying risk factors and predicting the non-return risk of deposits, this study contributes to an informed policy decision in planning and practicing Chonsei contracts in the Korean housing market.

ESG rating confusion and bond spreads

This paper investigates the influence of ESG rating confusion on bond spreads, an area of growing importance in sustainable finance. Despite the recognition of ESG rating confusion, their effects on the bond market remain less explored. Leveraging a corporate bond pricing model, we reveal that ESG rating confusion widens the bond spread. Analyzing data from China, we find that both primary and secondary bond markets are affected, with the latter experiencing a more significant impact. Our study emphasizes the role of information asymmetry and investor preferences, outweighing the issuer’s financial position in driving these effects. Furthermore, we identify environmental and weighting inconsistencies as crucial sources of ESG rating confusion. The findings underscore the necessity of a standardized ESG rating system to enhance investor confidence and support ESG-related project financing. A cohesive rating approach among agencies correlates with narrower bond spreads, thereby fostering bond market efficiency and promoting ESG investment practices.

The role of green reputation, carbon trading and government intervention in determining the green bond pricing: An externality perspective

Green bonds, due to their inherent externalities, pose challenges in achieving equilibrium prices under optimal public resource allocation and social welfare without governmental intervention. While numerous studies have explored green bond pricing via government subsidies and other strategies, this paper diverges by targeting the equilibrium price of green bonds as the optimal public resource allocation, specifically from an externality's standpoint. We integrate factors such as the cap-and-trade system, carbon taxes, government subsidies, and green reputation into the fractional jump-diffusion model. This is further combined with real options theory and the fast Fourier transform method to derive the green bond pricing equation. Through optimization techniques, we delve into the influences of various factors and the interplay of different governmental strategies. Numerical findings suggest that, in an ideal scenario where the government grants adequate carbon allowances to green bond issuers, the carbon emission trading mechanism can facilitate the bond in achieving its ideal equilibrium price. Conversely, when the government is restricted by carbon constraints, preventing green bond issuers from acquiring ample carbon allowances, supplementary measures like government subsidies and carbon tax policies can complement the cap-and-trade system, ensuring green bonds attain their ideal equilibrium price. Furthermore, given the carbon emission reduction level of green projects, our model can determine the optimal government subsidy ratio and the ideal carbon tax price.

Relation Between Inflation and Capital Market in India

Capital market has significant influence on the inflationary situation in an economy both in the short run and in the long run. The general determinants which affect capital market are share indices, stock prices, prices of gold and silver, interest rate, exchange rate and even money supply can be treated as influencing variable when money is treated as capital. In this paper the author examined the impact of capital market on inflation in India during 1980-2022 taking CPI and WPI as the indicators of inflation and gold price, silver price, BSE index, BSE market capitalization, money supply and rupee dollar nominal exchange rate are considered as the indicators of capital market. Augmented Dickey-Fuller test was applied to verify unit root for all variables including break unit roots and the author calculated growth rates during 1980 – 2022 using the simple semi-log linear trend model. Author applied Johansen cointegration test and vector error correction models among the variables in both the cases of CPI and WPI and calculated short run causality through Wald test and long run causality through cointegrating equation. In VECM, the paper found one significant cointegrating equation in case of CPI and found three significant cointegrating equations in WPI. The paper found that CPI has long run causal relationships with BSE index, BSE market capitalisation, money supply and rupee dollar exchange rate and WPI has long run cointegrating relationships with BSE market capitalisation, money supply and rupee dollar exchange rate respectively. Rupee dollar exchange rate has short run causal relationships with CPI and again CPI and BSE index has bidirectional short run causality. According to impulse response functions, the responses of CPI to gold price, BSE market capitalization and money supply have been approaching towards equilibrium but finally diverged away from equilibrium. There are significant short run causalities from BSE index and money supply to WPI. The impulse response of WPI to gold price, silver price, BSE index, BSE market capitalisation and exchange rate of rupee have been converging towards equilibrium many times although diverged finally. The model can be modified by including interest rate, share prices, bond prices under monthly data analysis.

Forecasting the South African Real Spot-rate Curve During Covid-19 Stress

Purpose: The aim of this study is to investigate the performance of forecasting the South African real spot rate curve using different mathematical term-structure models during the Covid-19 period. This study follows previous studies by Reid (2009) and Mashoene et al., 2021) where dynamic term-structure models performed well compared to static term-structure models over normal market conditions. For high volatility, normality often breaks, which might result in a need for recalibration in the currently used methodology or a total change in the methodology.
 Methodology: This study explores the dynamism of both dynamic term-structure models, which follow the Nelson-Siegel framework, and static term-structure models with the option of recalibration of model parameters to account for a change in macro-economic dynamics brought by the effect of the Covid-19 pandemic. The forecasting exercise was applied to term-structure models following the Nelson-Siegel framework using AR(1) process.
 Findings: The incorporation of macroeconomic factors performed poorly in estimating future model parameters. This could be attributable to the effect of global stress, which resulted in poor linear relationships between macroeconomic and financial factors. Recalibration process on static Nelson-Siegel term-structure models improved the models' performance, especially during the high volatility/period of total economic shutdown due to the Covid-19 pandemic.
 Implications: The application of static Nelson-Siegel term-structure models with recalibrated parameters might improve bond pricing processes and risk management strategies in emerging markets, especially during market stress.

Mitigating wildfire losses via insurance‐linked securities: Modeling and risk management perspectives

AbstractThis paper investigates the use of catastrophe (CAT) bonds as a risk management tool for wildfires. We introduce a set of Bayesian dynamic models designed to accurately represent wildfire losses, allowing a thorough examination of wildfire CAT bond pricing and hedge effectiveness. Our model captures crucial attributes of wildfire data, such as zero inflation, overdispersion, temporal fluctuations, and spatial dependence. Employing extensive quantitative analyses of US wildfire data, we highlight that CAT bonds can substantially mitigate tail risk associated with insurers' liability. Importantly, index‐based CAT bonds, drawing their payouts from aggregate wildfire losses over a larger geographical scope than an insurer's operational area, also provide effective hedges. Our research underscores the potential of wildfire CAT bonds as an enhancement to traditional reinsurance strategies, offering insurers an improved means to manage and mitigate wildfire exposures amidst inherent uncertainties.

Predicting Chinese stock prices using convertible bond: an evidence-based neural network approach

PurposeThe primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.Design/methodology/approachThe authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.FindingsThe results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.Originality/valueThis study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.

Mutual Fund Fragility, Dealer Liquidity Provision, and the Pricing of Municipal Bonds

Against the backdrop of COVID-19, we study how the interactions of mutual funds and dealers introduce fragility to the municipal bond market and induce lasting market impacts. During the crisis, trading surges, whereas dealers’ liquidity provision plunges for mutual-fund-held bonds, leading to greater price depressions in these bonds. Importantly, the crisis reshapes the market’s perceptions of mutual fund fragility risks, with the aftermath-yield spreads widening significantly more for bonds with greater mutual fund exposures. Such postcrisis pricing effects reflect dealers’ continued reluctance to provide liquidity for mutual fund–held bonds, and they are stronger for bonds whose mutual fund holders are more susceptible to investor runs. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4912 .

Using Mortgage Rates to Explain Risk Premiums in an Introductory Finance Course

Devising an effective way to teach interest rate determinants and help students understand various risk premiums is an essential part of any introductory finance course. Unfortunately, because these topics are commonly taught in conjunction with the introduction of bonds, students often become confused while trying to simultaneously learn new terminology and concepts related to bond pricing. In this paper, we describe a more student friendly approach to teaching interest rate determinants. We believe students gain a better understanding of risk premiums when related to loan types they are already somewhat familiar with. Specifically, this paper describes how to use a simple comparison of various mortgage rates to more clearly explain concepts such as default risk, interest rate risk, and liquidity risk. This approach focuses students' attention on the concepts at hand rather than confusing them with multiple high level applications at one time.

A Regional Catastrophe Bond Pricing Model and Its Application in Indonesia’s Provinces

The national scale of catastrophic losses risk linked to state catastrophe bonds (SCB) is enormous. It can reduce investors’ interest in buying them because the capital required and the loss probability are also significant. To overcome this, the SCB can be made on a smaller regional scale, known as a regional catastrophe bond (RCB). Through RCBs, the catastrophic loss risk investors bear becomes smaller, which can increase investors’ interest in buying them. Unfortunately, RCB issuance faced a fundamental obstacle, where its complex pricing model needed further study. Therefore, this study aims to model it. The model uniquely involves the inflation rate modeled using the Fisher equation and the nonbinary scheme of coupon and redemption value payments modeled by a compound Poisson process. In addition, the model is applied to Indonesia’s catastrophe data, resulting in all provinces’ RCB price estimation and the effects of several variables on RCB price. This research can guide the RCB pricing process of the country’s regions. The estimated RCB prices can be used by Indonesia’s government if RCBs are to be issued one day. Finally, the effects of the inflation rate, catastrophe intensity, and geographical location on RCB prices can guide investors in selecting bond portfolios.

Original sin and the great depression

Do exchange rate movements matter for how markets price foreign currency denominated sovereign bonds? High-frequency bond price data from 1931 show that depreciation against the dollar/gold was associated with elevated risk premia on US dollar/gold public debt. We use a theoretical model to illustrate how foreign currency debt influences exchange rate policy and foreign currency bond prices. We use these theoretical results, the timing of sterling's devaluation in September 1931, and historically determined fundamentals to identify the impact of exchange rate policy on hard-currency bond yields in the Great Depression.

A Study on Performance Evaluation of Balanced Investment Schemes Offered by Selected Asset Management Companies

Investing in mutual funds and diversifying the investment portfolio are two critical strategies for achieving long-term financial success. Mutual funds pool money from multiple investors to invest in a diversified portfolio of stocks, bonds, or other assets. This diversification helps reduce the impact of individual stock or bond price fluctuations, thus mitigating market risk. Mutual funds are managed by experienced fund managers who conduct extensive research and analysis. These professionals make informed investment decisions, saving investors from the need to constantly monitor the market and individual securities. Diversification allows investors to balance risk and return according to their risk tolerance. By spreading investments across different asset classes and risk levels, individuals can achieve their desired risk-return profile. By harnessing the expertise of professional fund managers and diversifying across various assets, investors can navigate market fluctuations with confidence while working toward a more secure financial future. It also contributes to the field of finance by offering a comprehensive analysis of the performance of balanced mutual fund schemes, employing a range of evaluation tools, and emphasizing the critical interplay between risk and return in the investment landscape. Keywords: Balanced mutual fund scheme, Performance evaluation, Asset Management Companies (AMCs), evaluation metrics.

Pricing Pandemic Bonds under Hull–White & Stochastic Logistic Growth Model

Pandemic bonds can be used as an effective tool to mitigate the economic losses that governments face during pandemics and transfer them to the global capital market. Once considered as an “uninsurable” event, pandemic bonds caught the attention of the world with the issuance of pandemic bonds by the World Bank in 2017. Compared to other CAT bonds, pandemic bonds received less attention from actuaries, industry professionals, and academic researchers. Existing research focused mainly on how to bring epidemiological parameters to the pricing mechanism through compartmental models. In this study, we introduce the stochastic logistic growth model-based pandemic bond pricing framework. We demonstrate the proposed model with two numerical examples. First, we calculate what investor is willing to pay for the World Bank issued pandemic bond while accounting for possible future pandemic, but require to have the same yield to maturity when no pandemic is there, and without using COVID-19 data. In the second example, we calculate the fair value of a pandemic bond with characteristics similar to the World Bank issued pandemic bond, but using COVID-19 data. The model can be used as an alternative to epidemic compartmental model-based pandemic bond pricing mechanisms.

Implementation of the Labor Laws in Indonesia for Formulation of Pension Reserves

This study aims to modify the calculation of severance reserves, the benefits of which are paid in accordance with Law number 6 of 2023 concerning Stipulation of a Substitute Law on Job Creation. This study seeks to modify the Projected Unit Credit calculation technique by considering Pension Constant which consists of Pension Factor, Work Term Reward Factor and Entitlement Compensation Pay Factor. In this research, the calculation of severance reserves is applied to PT A which has 24 permanent employees with an average age of employees is 29.67 years and the average work term is 5.83 years. The employee's salary during the valuation period has an average of IDR 2,776,214. This research tries to find the work mortality based on the number of working population and laid-off workers in Indonesia to be the basis for termination probability. The interest rate used in this research is the average return on bonds published by the Indonesian Bond Pricing Agency (IBPA) in five years, which is 0.063467. This research used TMI IV to define the probability of survival and death. This assumption can be used for all companies to maintain the company's financial stability by allocating reserves to pay severance pay in three possible cases, namely severance benefits upon retirement, severance benefit if employee leaves the company between valuation year and severance benefit if employee dies between the valuation year. Based on the calculation results, the reserve that needs to be stored is Rp. 1,186,723,329.18

Pricing Multi-Event-Triggered Catastrophe Bonds Based on a Copula–POT Model

The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative and systemic trigger mechanism for pricing catastrophic bonds triggered by multiple events with an extreme dependence structure. Due to the bond’s low cashflow contingencies and the CAT bond’s high return, the multiple-event CAT bond may successfully transfer the catastrophe risk to the huge financial markets to meet the diversification of capital allocations for most potential investors. The designed hybrid trigger mechanism helps reduce the moral hazard and increase the bond’s attractiveness with a lower trigger likelihood, displaying the determinants of the wiped-off coupon and principal by both the magnitude and intensity of the natural disaster events involved. As the trigger indicators resulting from the potential catastrophic disaster might be associated with heavy-tailed margins, nested Archimedean copulas are introduced with marginal distributions modeled by a POT-GP distribution for excess data and common parametric models for moderate risks. To illustrate our theoretical pricing framework, we conduct an empirical analysis of pricing a three-event rainstorm CAT bond based on the resulting losses due to rainstorms in China during 2006–2020. Monte Carlo simulations are carried out to analyze the sensitivity of the rainstorm CAT bond price in trigger attachment levels, maturity date, catastrophe intensity, and numbers of trigger indicators.

Pricing of Credit Risk Derivatives with Stochastic Interest Rate

This paper deals with a credit derivative pricing problem using the martingale approach. We generalize the conventional reduced-form credit risk model for a credit default swap market, assuming that the firms’ default intensities depend on the default states of counterparty firms and that the stochastic interest rate follows a jump-diffusion Cox–Ingersoll–Ross process. First, we derive the joint Laplace transform of the distribution of the vector process (rt,Rt) by applying piecewise deterministic Markov process theory and martingale theory. Then, using the joint Laplace transform, we obtain the explicit pricing of defaultable bonds and a credit default swap. Lastly, numerical examples are presented to illustrate the dynamic relationships between defaultable securities (defaultable bonds, credit default swap) and the maturity date.

Currency Redenomination Risk

Abstract A eurozone exit or breakup exposes bondholders to currency redenomination risk. I quantify redenomination risk since the sovereign debt crisis: It contributes substantially to credit spreads around changes in government in France and Italy. Bond prices suggest that markets have priced a potential Italian exit as isolated, and a French one as a breakup. Unlike conventional default risk, redenomination risk can be negative depending on the strength of the national “shadow” currency. Countries with strong shadow currencies earn breakup-insurance premia from the eurozone analog of “exorbitant privilege.” Yield effects are quantitatively large for implied exit probabilities as low as 1%.